"""simple docstring"""



import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy



def 	_lowerCamelCase(	a   ,    a				):
       assert isinstance(a   ,    a				)
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype



@require_sqlalchemy
@pytest.mark.parametrize("keep_in_memory"   ,    [False, True]				)
def 	_lowerCamelCase(	a   ,    a   ,    a   ,    a				):
       __a       =    tmp_path / "cache"
       __a       =    {"col_1": "string", "col_2": "int64", "col_3": "float64"}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              __a       =    SqlDatasetReader(
                  "dataset"   ,    "sqlite:///" + sqlite_path   ,    cache_dir=a   ,    keep_in_memory=a				).read()
       _check_sql_dataset(a   ,    a				)



@require_sqlalchemy
@pytest.mark.parametrize(
    "features"   ,    [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]   ,    )
def 	_lowerCamelCase(	a   ,    a   ,    a   ,    a				):
       __a       =    tmp_path / "cache"
       __a       =    {"col_1": "string", "col_2": "int64", "col_3": "float64"}
       __a       =    features.copy() if features else default_expected_features
       __a       =    (
           Features({feature: Value(a				) for feature, dtype in features.items()}				) if features is not None else None
       )
       __a       =    SqlDatasetReader("dataset"   ,    "sqlite:///" + sqlite_path   ,    features=a   ,    cache_dir=a				).read()
       _check_sql_dataset(a   ,    a				)



def 	_lowerCamelCase(	a				):
       with contextlib.closing(sqlitea.connect(a				)				) as con:
              __a       =    con.cursor()
              cur.execute("SELECT * FROM dataset"				)
              for row in cur:
                     yield row



@require_sqlalchemy
def 	_lowerCamelCase(	a   ,    a   ,    a				):
       __a       =    tmp_path / "cache"
       __a       =    os.path.join(a   ,    "tmp.sql"				)
       __a       =    SqlDatasetReader("dataset"   ,    "sqlite:///" + sqlite_path   ,    cache_dir=a				).read()
       SqlDatasetWriter(a   ,    "dataset"   ,    "sqlite:///" + output_sqlite_path   ,    num_proc=1				).write()

       __a       =    iter_sql_file(a				)
       __a       =    iter_sql_file(a				)

       for rowa, rowa in zip(a   ,    a				):
              assert rowa == rowa



@require_sqlalchemy
def 	_lowerCamelCase(	a   ,    a   ,    a				):
       __a       =    tmp_path / "cache"
       __a       =    os.path.join(a   ,    "tmp.sql"				)
       __a       =    SqlDatasetReader("dataset"   ,    "sqlite:///" + sqlite_path   ,    cache_dir=a				).read()
       SqlDatasetWriter(a   ,    "dataset"   ,    "sqlite:///" + output_sqlite_path   ,    num_proc=2				).write()

       __a       =    iter_sql_file(a				)
       __a       =    iter_sql_file(a				)

       for rowa, rowa in zip(a   ,    a				):
              assert rowa == rowa




@require_sqlalchemy
def 	_lowerCamelCase(	a   ,    a   ,    a				):
       __a       =    tmp_path / "cache"
       __a       =    os.path.join(a   ,    "tmp.sql"				)
       __a       =    SqlDatasetReader("dataset"   ,    "sqlite:///" + sqlite_path   ,    cache_dir=a				).read()
       with pytest.raises(a				):
              SqlDatasetWriter(a   ,    "dataset"   ,    "sqlite:///" + output_sqlite_path   ,    num_proc=0				).write()

"""simple docstring"""



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   SCREAMING_SNAKE_CASE__:List[Any]									=			"""platform"""

   import jax
   import jax.numpy as jnp

   from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
       FlaxBlenderbotSmallForConditionalGeneration,
       FlaxBlenderbotSmallModel,
       shift_tokens_right,
   )



def 	_lowerCamelCase(	a   ,    a   ,    a=None   ,    a=None   ,    a=None   ,    a=None   ,    a=None   ,    a=None   ,    ):
       if attention_mask is None:
              __a       =    np.where(input_ids != config.pad_token_id   ,    1   ,    0				)
       if decoder_attention_mask is None:
              __a       =    np.where(decoder_input_ids != config.pad_token_id   ,    1   ,    0				)
       if head_mask is None:
              __a       =    np.ones((config.encoder_layers, config.encoder_attention_heads)				)
       if decoder_head_mask is None:
              __a       =    np.ones((config.decoder_layers, config.decoder_attention_heads)				)
       if cross_attn_head_mask is None:
              __a       =    np.ones((config.decoder_layers, config.decoder_attention_heads)				)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": attention_mask,
       }






class  snake_case__      :


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase=13    ,		lowerCamelCase=7    ,		lowerCamelCase=True    ,		lowerCamelCase=False    ,		lowerCamelCase=99    ,		lowerCamelCase=16    ,		lowerCamelCase=2    ,		lowerCamelCase=4    ,		lowerCamelCase=4    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=32    ,		lowerCamelCase=2    ,		lowerCamelCase=1    ,		lowerCamelCase=0    ,		lowerCamelCase=0.02    ,		):
        __a       =    parent
        __a       =    batch_size
        __a       =    seq_length
        __a       =    is_training
        __a       =    use_labels
        __a       =    vocab_size
        __a       =    hidden_size
        __a       =    num_hidden_layers
        __a       =    num_attention_heads
        __a       =    intermediate_size
        __a       =    hidden_act
        __a       =    hidden_dropout_prob
        __a       =    attention_probs_dropout_prob
        __a       =    max_position_embeddings
        __a       =    eos_token_id
        __a       =    pad_token_id
        __a       =    bos_token_id
        __a       =    initializer_range


 def 						a__   ( self						):
        __a       =    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,		self.vocab_size						)    ,		3    ,		self.vocab_size						)
        __a       =    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,		dtype=np.intaa						))    ,		-1						)

        __a       =    shift_tokens_right(lowerCamelCase    ,		1    ,		2						)

        __a       =    BlenderbotSmallConfig(
            vocab_size=self.vocab_size    ,		d_model=self.hidden_size    ,		encoder_layers=self.num_hidden_layers    ,		decoder_layers=self.num_hidden_layers    ,		encoder_attention_heads=self.num_attention_heads    ,		decoder_attention_heads=self.num_attention_heads    ,		encoder_ffn_dim=self.intermediate_size    ,		decoder_ffn_dim=self.intermediate_size    ,		dropout=self.hidden_dropout_prob    ,		attention_dropout=self.attention_probs_dropout_prob    ,		max_position_embeddings=self.max_position_embeddings    ,		eos_token_id=self.eos_token_id    ,		bos_token_id=self.bos_token_id    ,		pad_token_id=self.pad_token_id    ,		initializer_range=self.initializer_range    ,		use_cache=lowerCamelCase    ,		)
        __a       =    prepare_blenderbot_inputs_dict(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)
        return config, inputs_dict


 def 						a__   ( self						):
        __a ,   __a       =    self.prepare_config_and_inputs()
        return config, inputs_dict


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    20
        __a       =    model_class_name(lowerCamelCase						)

        __a       =    model.encode(inputs_dict["input_ids"]						)

        __a ,   __a       =    (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        __a       =    model.init_cache(decoder_input_ids.shape[0]    ,		lowerCamelCase    ,		lowerCamelCase						)
        __a       =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,		dtype="i4"						)

        __a       =    jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]    ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,		)
        __a       =    model.decode(
            decoder_input_ids[:, :-1]    ,		lowerCamelCase    ,		decoder_attention_mask=lowerCamelCase    ,		past_key_values=lowerCamelCase    ,		decoder_position_ids=lowerCamelCase    ,		)

        __a       =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,		dtype="i4"						)
        __a       =    model.decode(
            decoder_input_ids[:, -1:]    ,		lowerCamelCase    ,		decoder_attention_mask=lowerCamelCase    ,		past_key_values=outputs_cache.past_key_values    ,		decoder_position_ids=lowerCamelCase    ,		)

        __a       =    model.decode(lowerCamelCase    ,		lowerCamelCase						)

        __a       =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
        self.parent.assertTrue(diff < 1E-3    ,		msg=F"Max diff is {diff}"						)






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    20
        __a       =    model_class_name(lowerCamelCase						)

        __a       =    model.encode(inputs_dict["input_ids"]						)

        __a ,   __a       =    (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        __a       =    jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
            ]    ,		axis=-1    ,		)

        __a       =    model.init_cache(decoder_input_ids.shape[0]    ,		lowerCamelCase    ,		lowerCamelCase						)
        __a       =    jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]    ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,		)

        __a       =    model.decode(
            decoder_input_ids[:, :-1]    ,		lowerCamelCase    ,		decoder_attention_mask=lowerCamelCase    ,		past_key_values=lowerCamelCase    ,		decoder_position_ids=lowerCamelCase    ,		)
        __a       =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,		dtype="i4"						)
        __a       =    model.decode(
            decoder_input_ids[:, -1:]    ,		lowerCamelCase    ,		past_key_values=outputs_cache.past_key_values    ,		decoder_attention_mask=lowerCamelCase    ,		decoder_position_ids=lowerCamelCase    ,		)

        __a       =    model.decode(lowerCamelCase    ,		lowerCamelCase    ,		decoder_attention_mask=lowerCamelCase						)

        __a       =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
        self.parent.assertTrue(diff < 1E-3    ,		msg=F"Max diff is {diff}"						)






@require_flax
class  snake_case__      (       unittest.TestCase       ):
 _snake_case       :					Dict							  =						99


 def 						a__   ( self						):
        __a       =    np.array(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  # note padding
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ]    ,		dtype=np.intaa    ,		)

        __a       =    input_ids.shape[0]
        __a       =    BlenderbotSmallConfig(
            vocab_size=self.vocab_size    ,		d_model=24    ,		encoder_layers=2    ,		decoder_layers=2    ,		encoder_attention_heads=2    ,		decoder_attention_heads=2    ,		encoder_ffn_dim=32    ,		decoder_ffn_dim=32    ,		max_position_embeddings=48    ,		eos_token_id=2    ,		pad_token_id=1    ,		bos_token_id=0    ,		)
        return config, input_ids, batch_size


 def 						a__   ( self						):
        __a ,   __a ,   __a       =    self._get_config_and_data()
        __a       =    FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase						)
        __a       =    lm_model(input_ids=lowerCamelCase						)
        __a       =    (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs["logits"].shape    ,		lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    BlenderbotSmallConfig(
            vocab_size=self.vocab_size    ,		d_model=14    ,		encoder_layers=2    ,		decoder_layers=2    ,		encoder_attention_heads=2    ,		decoder_attention_heads=2    ,		encoder_ffn_dim=8    ,		decoder_ffn_dim=8    ,		max_position_embeddings=48    ,		)
        __a       =    FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase						)
        __a       =    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,		dtype=np.intaa						)
        __a       =    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,		dtype=np.intaa						)
        __a       =    lm_model(input_ids=lowerCamelCase    ,		decoder_input_ids=lowerCamelCase						)
        __a       =    (*summary.shape, config.vocab_size)
        self.assertEqual(outputs["logits"].shape    ,		lowerCamelCase						)






 def 						a__   ( self						):
        __a       =    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,		dtype=np.intaa						)
        __a       =    shift_tokens_right(lowerCamelCase    ,		1    ,		2						)
        __a       =    np.equal(lowerCamelCase    ,		1						).astype(np.floataa						).sum()
        __a       =    np.equal(lowerCamelCase    ,		1						).astype(np.floataa						).sum()
        self.assertEqual(shifted.shape    ,		input_ids.shape						)
        self.assertEqual(lowerCamelCase    ,		n_pad_before - 1						)
        self.assertTrue(np.equal(shifted[:, 0]    ,		2						).all()						)






@require_flax
class  snake_case__      (       snake_case_,						unittest.TestCase,						snake_case_       ):
 _snake_case       :					Any							  =						True
 _snake_case       :					int							  =						(
     (
         FlaxBlenderbotSmallModel,
         FlaxBlenderbotSmallForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 _snake_case       :					str							  =						(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


 def 						a__   ( self						):
        __a       =    FlaxBlenderbotSmallModelTester(self						)


 def 						a__   ( self						):
        __a ,   __a       =    self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)


 def 						a__   ( self						):
        __a ,   __a       =    self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)


 def 						a__   ( self						):
        __a ,   __a       =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__						):
                      __a       =    self._prepare_for_class(lowerCamelCase    ,		lowerCamelCase						)
                      __a       =    model_class(lowerCamelCase						)

                      @jax.jit
                      def encode_jitted(lowerCamelCase    ,		lowerCamelCase=None    ,		**lowerCamelCase						):
                             return model.encode(input_ids=lowerCamelCase    ,		attention_mask=lowerCamelCase						)

                      with self.subTest("JIT Enabled"						):
                             __a       =    encode_jitted(**lowerCamelCase						).to_tuple()

                      with self.subTest("JIT Disabled"						):
                             with jax.disable_jit():
                                    __a       =    encode_jitted(**lowerCamelCase						).to_tuple()

                      self.assertEqual(len(lowerCamelCase						)    ,		len(lowerCamelCase						)						)
                      for jitted_output, output in zip(lowerCamelCase    ,		lowerCamelCase						):
                             self.assertEqual(jitted_output.shape    ,		output.shape						)


 def 						a__   ( self						):
        __a ,   __a       =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__						):
                      __a       =    model_class(lowerCamelCase						)
                      __a       =    model.encode(inputs_dict["input_ids"]    ,		inputs_dict["attention_mask"]						)

                      __a       =    {
                          "decoder_input_ids": inputs_dict["decoder_input_ids"],
                          "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                          "encoder_outputs": encoder_outputs,
                      }

                      @jax.jit
                      def decode_jitted(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
                             return model.decode(
                                 decoder_input_ids=lowerCamelCase    ,		decoder_attention_mask=lowerCamelCase    ,		encoder_outputs=lowerCamelCase    ,		)

                      with self.subTest("JIT Enabled"						):
                             __a       =    decode_jitted(**lowerCamelCase						).to_tuple()

                      with self.subTest("JIT Disabled"						):
                             with jax.disable_jit():
                                    __a       =    decode_jitted(**lowerCamelCase						).to_tuple()

                      self.assertEqual(len(lowerCamelCase						)    ,		len(lowerCamelCase						)						)
                      for jitted_output, output in zip(lowerCamelCase    ,		lowerCamelCase						):
                             self.assertEqual(jitted_output.shape    ,		output.shape						)






 @slow
 def 						a__   ( self						):
        for model_class_name in self.all_model_classes:
               __a       =    model_class_name.from_pretrained("facebook/blenderbot_small-90M"						)
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               __a       =    np.ones((1, 1)						) * model.config.eos_token_id
               __a       =    model(lowerCamelCase						)
               self.assertIsNotNone(lowerCamelCase						)
